Protecting Essential Data with Security Analytics

Every day 2.5 quintillion bytes of data are produced. Some of it is important (and private), like the most essential of financial and medical records. Some of it, like the cleaning path taken by an IoT vacuum is largely useless (or is it?). And all of it can become vulnerable without the right security effort.

Subsequently, there is a growing need for us to find new and better ways to protect our most sensitive data from a host of digital threats rise. Cybersecurity analytics can ultimately help to lay the foundation for large scale data protection. Some questions that this post sets out to answer include:

What is Cybersecurity Analytics?

Every day millions of cyber attacks are successfully executed around the world. Though it may be impossible to 100% prevent them all, we can certainly learn from them to help develop better ways to stay protected. Cybersecurity analytics studies the digital trail left behind by cyber criminals to help better understand weaknesses and how to prevent similar losses in the future.

Comparing Cybersecurity and Information Security

Though these terms are often used interchangeably, they don’t mean the same thing exactly. In information security, the biggest concern is to safeguard data from illegal access of any kind. In cybersecurity, the biggest concern is to safeguard data from illegal digital access. In other words, cybersecurity works to protect digital information, whereas information security works to protect all information, regardless of whether it is kept digitally or not.

The Benefits of Cybersecurity Analytics

For organizations aiming to become more data-driven entities, it is no surprise that analytics should also include cybersecurity concerns. Some benefits associated with cybersecurity analytics are:

A more visual analytics process: There is an existing shortage of data security professionals, so the need for clear and understandable tools and platforms is essential. Today, new systems enable business users to better understand relationships and historical trends.

Having a more holistic view of security considerations: Understanding how an attack fits in context with existing systems, protocols, and traffic can provide valuable insights for businesses trying to pinpoint better strategies.

Enhanced data enrichment capacity: With the help of data security professionals that tag, index, and query data elements, analysts are able to add context to the data, making it more useful and actionable.

Aiding IT departments: Unless you have an endless budget, chances are you want to avoid overburdening your IT department with more and bigger problems. With cybersecurity analytics, their focus can be shifted to the most important areas and on prevention.

Utilizing ignored data sources: Many organizations have a number of underutilized data sources that could hold the key to understanding security threats. With effective cybersecurity analytics practices, more and more of these sources can be integrated into existing security efforts.

Cybersecurity Analytics Use Cases

In a world that increasingly relies on flexibility and mobile connectivity, cybersecurity analytics is becoming more and more beneficial. Some key challenges that cybersecurity analytics helps to meet include:

Many more businesses are integrating BYOD (bring your own device) policies into their daily activities. While this does increase the opportunities for productivity for employees, it does also open up additional vulnerabilities.

The increased complexity of attacks, in addition to the amount of time they are often able to go undetected.

The ever-growing volume of data, from many different sources creates an environment where speed and accuracy are indispensable.

A quick response can mean the difference between manageable threat and major disaster.

AI & Cybersecurity

AI can be extremely helpful with cybersecurity analytics and security analytics as a whole. Though many of these solutions don’t operate entirely independently, they instead are able to provide the necessary analytics ‘horsepower’ to existing technologies, leading to more effective practices. This ultimately leads to enhanced security and improved operational efficiency in an organization.

AI knowledge graphs (as opposed to knowledge bases) can also greatly improve the effectiveness of cybersecurity analytics. This is because they can act as repositories for collecting the enormous amount of data being produced constantly. However, knowledge graphs also serve to identify patterns and relationships in security issues that matter most to an organization. This can empower more effective predictive analytics to help avoid major security problems prior to their exposure.

Machine learning has also demonstrated value in cybersecurity analytics in efforts such as behavior analysis. It can also be useful in security operations such as countermeasures, inline inspection, and SOC analytics in order to seamlessly integrate updated detection rules and concepts.

Cybersecurity Analytics Best Practices

There are a number of cybersecurity analytics best practices that can be implemented in order to help maximize the effectiveness of your efforts.

1. Collect event data from throughout an organization’s network into a single access point or pane. This is integral for better analysis, implementation, and compliance reporting.

2. Use different forms of threat detection and learning in monitoring and analysis. For example, a statistical approach might be more effective in analyzing a suspicious spurt of network traffic. Alternatively, machine learning may recognize traffic patterns indicating malicious activity. The right combination for any organization varies depending on security needs.

3. Consider using cloud-based services in order to build a more comprehensive dataset.

4. Use information gained from previous (or emerging) threats or as issued in mandates such as GDPR to aid analytics through improved context.

Cybersecurity and Security Analytics Pitfalls

Though cybersecurity analytics has demonstrated a wide range of benefits, it is not without its own pitfalls. Some common drawbacks and challenges related to cybersecurity analytics include:

As our knowledge and understanding grows with the help of analytics, there continues to be an equally as strong and diverse threat growing from adversaries. This requires a need to be constantly updating analytics practices; even when AI is available to help.

There is a shortage of skilled professionals in the cybersecurity analytics field. This leads to an increased volume of vulnerabilities throughout organizations. Given the sensitivity of many cyber attacks, the need for human intervention and understanding through is key.

For many, the rapid integration of operational security analytics is a major objective. This means that the time necessary to deploy a new cybersecurity analytics program and related software can feeling daunting; particularly as it is scaled up in larger organizations.

Two Types of Cybersecurity Analytics Solutions

There are a number of methodologies related to cybersecurity analytics that are valuable for organizations today, however there are two in particular that are particularly useful.

Predictive analytics: Being a more proactive entity means executing a response before an attack has time to take hold. This means mapping patterns within an IT environment and understanding as many details as possible so that the moment something changes, you can respond in advance.

An additional benefit to a strong predictive analytics program is that it can automate a number of straightforward tasks that would otherwise be manage by IT. This can also help in alleviating ‘alert fatigue’ because many notifications are managed without the need for human intervention.

User & Entity Behavior Analytics (UEBA): It works by using advanced algorithms (often coupled with large-scale storage systems for data warehousing purposes) to establish a baseline of certain activities routinely conducted by users or systems. It can then identify and alert upon behavioral anomalies which signify a deviation from these baselines

Cybersecurity Analytics Vendors & Tools

Gartner has suggested that by 2020 almost three quarters of all cybersecurity products will include some version of advanced analytics [1]. At this time, there are a number of different solutions available as part of a bigger platform or as standalone products. Some are even starting to include increasingly sophisticated AI capabilities.

It is important to consider that many tools related to security analytics either serve to respond or to provide analytics, not both. However, as cyber attacks become more sophisticated, the need for an instant, automated solution grows. This is because human speeds are subject to a much wider range of delays and challenges, whereas automated systems can stop a security threat before it becomes a disaster.

Some tools that can help in cybersecurity analytics include:

Name

Founded

Status

Number of Employees

Includes Automated Response

Acunetix Vulnerability Scanner

2005

Private

11-50

Yes

Cisco Stealthwatch

1984

Public

10,001+

No

Darktrace

2013

Private

501-1,000

Yes

EMC RSA Security Analytics NetWitness

1984

Public

1,001-5,000

No

FireEye Threat Analytics Platform

2004

Public

1,001-5,000

No

Juniper Networks JSA Series Secure Analytics

1996

Public

5,001-10,000

No

Netscout Arbor Networks ATLAS

1984

Public

1,001-5,000

No

Sophos UTM

1985

Public

1,001-5,000

Yes

Sumo Logic

2010

Private

201-500

Yes

How to Get Started with Cybersecurity Analytics

Building an effective cybersecurity analytics program requires careful planning and the integration of a wide range of employees and stakeholders. There are a number of strategies to aid in the successful creation of an analytics-driven security environment.

Introducing SOAPA

Cybersecurity analytics is not a practice that can be completed separately from operational efforts. To integrate these two activities, many organizations utilize Security Operations and Analytics Platform Architecture (SOAPA) as a model for their own structure.

The goal with SOAPA is to connect different cybersecurity tools into a system that is more efficient and effective. This also helps the system to stay effective as new sources and tools are integrated. Aside from data exchange, SOAPA provides a centralized view of all security activities; an invaluable resource for analytics. Some tools it integrates are:

Endpoint protection platforms (EPP)

Vulnerability scanners

Incident response platforms (IRPs)

Network security analytics

Anti-malware sandboxes

Threat intelligence

By launching a SOAPA model, organizations can often achieve the following results:

Faster threat detection and response time

Better operational efficiency in security tasks

Enhanced employee productivity

3 Steps to SOAPA and Beyond

There are a few steps that can be taken to achieve SOAPA and a more protected environment. These are:

Evaluate your existing infrastructure. As different tools were developed alongside with new threats, silos and similar problems evolved. Your evaluation should help you to determine your biggest challenges, including security process maturity and depth of knowledge within your team.

Apply best practices. Though there are differences between industries, general best practices related to cybersecurity analytics can be followed to help establish an executable strategy. Vendors often provide use cases and their common best practices to help organizations understand the different applications.

Integrate human and machine interaction. Finding ways to become more efficient in an environment with a limitless volume of data is integral for staying ahead. Machine learning and artificial intelligence can help to automate a number of analytical and operational tasks to help free up precious human time and energy.

Having a more secure business is only a small part of what it takes to be successful in today’s super competitive environment. Want to learn more about the technologies that are changing how organizations are run? Check out our blog. Or find your next AI vendor in our library of over 3,000 use cases.

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